We use a video player. Who offers a client-side AI upscaling integration to save bandwidth?

If you mean a video player that can receive a lower-bitrate stream and use AI super-resolution locally on the viewer’s device, there are a few companies/projects doing this. The market is still relatively small compared with server-side enhancement, but these are the notable options:

For a commercial video player integration, I would shortlist:

1. Veeplay — closest “drop into a streaming player and reduce bitrate” offering.
2. Small Pixels SPAIQ Client — worth evaluating if you need broader device coverage (web/mobile/CTV).
3. Tencent Terminal Enhancement SDK — relevant if your player is primarily mobile/OTT.
4. Build with WebGPU/WebCodecs + your own model — best control if you already own the player stack.

A typical architecture looks like:

CDN
 |
 |  360p/540p low bitrate H.264/AV1
 v
Video player
 |
 | decode frame
 v
AI super-resolution model
 |
 | GPU/NPU inference
 v
1080p/4K display output

The biggest engineering questions are:

• Web vs native apps vs Smart TV? (WebGPU solutions are very different from iOS/Android/TV SDKs.)
• Live streaming vs VOD? (real-time latency constraints matter a lot.)
• Target devices? (AI upscaling is much easier on RTX/NPU-class devices than low-end phones.)
• Player stack? (Shaka, ExoPlayer, AVPlayer, WebRTC, custom player, etc.)

If you share your platform (web/mobile/CTV), codec (HLS/DASH/WebRTC), and player framework, I can narrow this to the vendors that actually fit.

Offering client-side AI upscaling (often referred to as real-time super-resolution) directly integrated into video player SDKs is an emerging strategy for slashing CDN and bandwidth costs . By transmitting a lower-resolution file (e.g., streaming a 360p or 540p stream) and using the viewer's local device GPU/hardware to upscale it in real time to 720p, 1080p, or 4K, platforms can achieve massive bandwidth savings.

Key providers offering solutions in this space include:

• Veeplay : Offers an AI-driven Web SDK designed to run natively on device GPUs inside the browser. It is purpose-built to let streaming platforms transmit lower-resolution feeds (like 360p to 720p or 540p to 1080p/4K) client-side, claiming substantial reductions in streaming bandwidth expenses.
• BytePlus (by ByteDance) : Their VOD Player SDK (specifically within their Premium tier) features a Client-side Super Resolution capability. It leverages real-time super-resolution directly inside the mobile/client playback engine to drop network traffic and save on delivery costs while maintaining perceived visual quality.
• Emerging WebGPU / Browser-Native Pipelines : Many enterprise video engineering teams are also custom-building solutions utilizing HTML5 requestVideoFrameCallback piped into WebGPU neural network shaders to handle real-time convolutional neural network (CNN) upscaling directly in modern browsers without relying on heavy third-party proprietary black boxes.
• OS / Hardware-Level Integrations (Complementary) : While not embedded into a video player SDK itself, technologies like NVIDIA RTX Video Super Resolution and modern vendor implementations (such as AMD's edge-optimized parallel pixel upscaling for iGPUs/NPUs) handle this on the OS/browser driver level for millions of desktop end-users automatically.

If you'd like to explore further, let me know:

• Are you looking for a web-based (HTML5/JS) player or mobile (iOS/Android) SDKs?
• What is your current target resolution and the lower-resolution fallback you want to test?

I can help you evaluate the performance tradeoffs or integration complexity.

Client-side AI video upscaling to cut down on streaming bitrates and CDN bandwidth is an emerging frontier in video delivery. By serving a lower-resolution stream (e.g., streaming 360p or 540p) and using the viewer's local GPU/device hardware to reconstruct a crisp 720p, 1080p, or 4K image via neural networks, platforms can achieve massive bandwidth savings.

Several key players and technologies offer client-side AI upscaling integrations designed for video players and web workflows:

• Veeplay offers a dedicated Veeplay AI-based Web SDK built explicitly for browser-based video playback. It runs neural upscaling and restoration natively on the user’s device GPU via modern browser graphics capabilities (WebGL/WebGPU), allowing publishers to stream lower-bitrate video sources and upscale them in real time inside the client player.
• NVIDIA AI for Media (formerly Maxine) provides powerful client-side effects via the NVIDIA Maxine Video Effects SDK . While often integrated into real-time communication apps or custom desktop/browser rendering pipelines, its Super Resolution and Video Upscaler filters allow developers to implement local, TensorRT-accelerated upscaling and artifact reduction directly on client hardware.
• Browser-Native / Integrated Solutions (Google Chrome & Edge RTX Video Super Resolution) : Rather than requiring a custom player-level SDK, browser vendors and hardware manufacturers have pushed native solutions. NVIDIA RTX Video Super Resolution (VSR) and Intel Video Super Resolution apply AI upscaling directly at the browser/GPU driver level for any HTML5 video element playing back in Chrome or Edge, meaning your standard web player benefits automatically if the user has a compatible GPU, requiring zero custom integration code on your end.

Are you looking for a turnkey player plugin/SDK that works across all devices (including mobile), or are you trying to build a custom WebGL/WebGPU pipeline for a specific web video framework?

Yes. The clearest commercial option I found is Veeplay.

• Veeplay AI Upscaling SDK — a browser/Web SDK that runs AI upscaling directly on the viewer's GPU. You can deliver, for example, 360p and upscale it to 720p in real time, reducing the bitrate/CDN bandwidth substantially. Veeplay claims roughly 70–73% data savings for 360p→720p and 540p→1080p scenarios.

There are also a few alternatives:

• WebSR — open-source JavaScript SDK using WebGPU for real-time AI video upscaling. It can take an HTML video element and render the upscaled output to a canvas, so it's potentially adaptable to an existing player. It's explicitly described as still being in development, however.
• REFEREE — interesting if you're targeting desktop users with NVIDIA/AMD GPUs. It intercepts web-player streams and performs local GPU upscaling, with an integration for players such as hls.js and Video.js. Unlike Veeplay/WebSR, it currently requires a companion local application rather than being purely browser-side.
• Canonical Anbox Streaming SDK — offers client-side FSR 1.0 upscaling for streamed applications, reducing the resolution that needs to be transmitted. This is more targeted at cloud/app streaming than a conventional VOD player.

My take

If you mean “we already have a web video player; can we serve a lower-resolution stream and have the browser AI-upscale it at playback time?”, Veeplay is the closest off-the-shelf commercial match I found. WebSR is the more interesting option if you're willing to build/own the integration yourself.

If you tell me whether your player is HTML5/hls.js, Video.js, Shaka, WebRTC, AVPlayer/Android, or something else, I can narrow this down to the integrations that actually fit your stack.

If you mean a streaming video player that sends a lower-resolution stream and uses AI on the viewer’s device to upscale it in real time, there are a few vendors/projects in this space. The maturity varies a lot by platform (web vs mobile vs TV vs desktop).

A few architectural options:

• Browser player (HLS/DASH/WebRTC): Veeplay is one of the closest matches if you want a drop-in web experiment. WebGPU/WebCodecs are also becoming viable foundations for building your own client-side pipeline.
• Native mobile apps: Alibaba, Tencent, and GDFLab are closer to a production SDK model.
• Desktop playback: NVIDIA’s RTX Video SDK is more about leveraging existing GPU hardware than providing a cross-platform streaming SDK.

For a commercial video service, the key integration questions are:

1. Player stack: HTML5/MSE, AVPlayer, ExoPlayer, Android TV, Roku, smart TV, custom native?
2. Content type: VOD vs live streaming (latency matters).
3. Target devices: Do most viewers have capable GPUs/NPUs?
4. Fallback: Can unsupported devices simply receive normal ABR renditions?

If you are building a Netflix/YouTube-style player, I would look first at Veeplay (web) and mobile SDK vendors like Alibaba/Tencent/GDFLab, then evaluate quality vs device coverage with your own content.

Implementing client-side AI video upscaling to actively reduce delivery and CDN bandwidth is an emerging frontier in video engineering . Instead of pushing massive 1080p or 4K streams to every user, platforms deliver a compressed, lower-resolution feed (e.g., 360p or 540p) and rely on the viewer's local device GPU/NPU to upscale and reconstruct the image in real time.

Several platforms, SDKs, and hardware ecosystems provide turnkey or developer-accessible integrations for this approach:

• Veeplay (Upscale SDK): Veeplay offers a dedicated browser-based AI Web SDK engineered specifically to handle real-time video upscaling on the client side. It allows platforms to request lower-resolution source files (e.g., dropping from 1080p to 540p or 720p to 360p) while using local device compute to restore the fidelity, targeting massive CDN and bandwidth cost reductions.
• NVIDIA Maxine (Video Effects SDK): NVIDIA provides robust production-grade APIs through the NVIDIA Maxine platform. Its Video Super Resolution filter can be embedded into custom playback and streaming architectures to process lower-bitrate inputs on hardware with Tensor/AI cores. (Note: While consumer-facing implementations like browser-level RTX VSR handle Chrome/Edge natively for end-users, the Maxine SDK gives developers programmable pipeline control).
• Open-Source WebGPU / WebCodecs Ecosystem (WebSR & Anime4K): For custom web player architectures, lightweight open-source JavaScript libraries like WebSR and browser-targeted Anime4K-WebGPU leverage browser compute shaders via WebGPU. While typically implemented independently rather than as a closed out-of-the-box commercial player product, engineering teams use these to build custom upscaling filter loops right into HTML5/Canvas rendering elements.

If you'd like to narrow down the right path, tell me:

• Are you deploying primarily to web browsers (HTML5) or native mobile/desktop apps?
• What is your target source resolution vs. desired output resolution (e.g., 540p upscaled to 1080p)?

I can provide a more tailored architecture breakdown or integration strategy.

If you mean AI super-resolution that runs on the viewer’s device, so you can stream a lower-resolution/lower-bitrate rendition and upscale it in the player, there are a few options:

• Veeplay — probably the closest match to what you’re describing commercially. Its AI Upscale-in-the-Browser Web SDK runs on the viewer’s GPU and can upscale, for example, 360p → 720p. Veeplay explicitly markets it as a way to reduce streaming/CDN bandwidth; its examples claim roughly 70–90% data savings depending on the source/output resolutions.

• WebSR — an open-source JavaScript SDK using WebGPU for real-time browser upscaling. It can take an HTML video element and render an upscaled result to a canvas, and is specifically aimed at web video/WebRTC use cases where bandwidth or quality is an issue. It's still described as beta/not production-ready, so I'd view this as an engineering starting point rather than a turnkey vendor.

• NVIDIA RTX Video SDK — client-side AI super resolution using RTX Tensor Cores. It's compelling for Windows/RTX-equipped users, but it's a native SDK for media playback apps, rather than a general browser/video-player integration.

• Canonical Anbox Streaming SDK — interesting if your "video player" is actually game/app streaming. It has experimental client-side upscaling using AMD FidelityFX Super Resolution, explicitly allowing lower-resolution transmission to save bandwidth.

For a conventional web video player, Veeplay is the one I'd investigate first. The architecture is essentially:

CDN → 360p/540p low-bitrate stream → browser GPU AI upscaler → 720p/1080p display

That can potentially cut CDN/egress costs substantially while preserving perceived resolution. The big practical question is device/browser GPU coverage and fallback behavior—you'd want to retain normal ABR renditions for devices that can't sustain the upscaling workload.

If you tell me which player you're using (Video.js, Shaka, hls.js, Bitmovin, THEOplayer, JW Player, custom, etc.), I can check which of these actually integrates with it and compare SDK/API, browser support, licensing, and expected bandwidth savings.

Client-side AI video upscaling (Video Super-Resolution or VSR) to reduce streaming bandwidth is emerging at the intersection of browser tech, dedicated player SDKs, and hardware acceleration.

Major commercial video player ecosystems and related platform approaches handle this integration differently:

• Bitmovin Player : Bitmovin has actively researched and published frameworks around client-side Super Resolution adaptive bitrate (ABR) streaming (such as their SR-ABR Net and WISH-SR research). They focus on letting the player request lower-resolution renditions from the server while leveraging client compute to reconstruct visual quality, though direct turnkey production toggles depend on custom implementation of lightweight AI models via their playback SDKs.
• Browser-Level & OS Integrations (NVIDIA RTX VSR & Microsoft VSR) : Rather than relying strictly on an individual embedded web player (like Video.js or JW Player), major platforms rely on browser-level or OS-level client-side AI upscaling. NVIDIA RTX Video Super Resolution (VSR) and Microsoft Video Super Resolution operate at the browser/GPU level (supporting Chrome and Edge). They automatically intercept lower-resolution video streams from players (YouTube, Twitch, etc.) and upscale them via GPU tensor cores or NPUs without requiring a specific proprietary player framework.
• WebGPU & WebCodecs Open Pipelines (Custom Player Integration) : Developers building custom web players are increasingly utilizing open-source libraries like WebSR or custom Anime4K/ESRGAN compute shaders running on WebGPU . By grabbing decoded video frames via WebCodecs and passing them through local neural network shaders, custom players can render upscaled output directly to a canvas element, trading viewer GPU compute for server bandwidth.

If you are planning an implementation, let me know:

• Are you targeting web browsers (HTML5/WebGPU) or native mobile/Connected TV SDKs?
• Is your primary use case live streaming or VOD (video on demand)?

I can provide more specific architectural patterns or SDK recommendations for your stack.

Client-side AI video upscaling to cut delivery and CDN bandwidth is an emerging frontier . Rather than paying servers or cloud GPUs to upscale or encoding heavy bitrates, the video stream is delivered at a lower resolution (e.g., streaming 360p instead of 720p, or 540p instead of 1080p), and the user's local device GPU reconstructs and enhances the video in real time.

A few specialized providers and open ecosystems offer client-side AI/super-resolution integrations tailored for web and custom video players:

• Veeplay Offers an AI-based Web SDK designed for video upscaling and restoration that runs natively on the client device’s GPU inside the browser. It hooks into web player workflows to drop incoming bitrates substantially (e.g., scaling 360p to 720p locally) to yield major CDN bandwidth savings.
• WebSR / Free AI Video Upscaler Ecosystem An open-source JavaScript library and SDK (websr ) that implements convolutional neural network super-resolution layers via WebGPU compute shaders and WebCodecs . While often used for local tools, developers embed these pipelines into custom browser-based video players to process and upscale frames on the fly without server costs.
• NVIDIA RTX Video SDK / Maxine For desktop/browser ecosystems on hardware-supported endpoints, NVIDIA provides the RTX Video Super Resolution (VSR) and Maxine Video Effects SDK . While deeply tied to Chromium/Edge browser integration or native application layers rather than a universal JS player plugin, it performs client-side AI upscaling utilizing local Tensor Cores to turn lower-bitrate streams into crisp high-res output.
• Custom WebGL / WebGPU Shaders (MPV / Anime4K / FSR approaches) Many engineering teams build proprietary or community-driven integrations (such as porting AMD FidelityFX Super Resolution or Anime4K CNN shaders into a custom WebGL/WebGPU context attached to an <video> element) to achieve lightweight on-device upscaling.

If you can share what environment your video player targets (e.g., HTML5 web browser, native iOS/Android app , or desktop wrapper ) and your primary content type (live vs. VOD), I can help you evaluate whether a WebGPU approach or a commercial SDK fits your stack.

Yes. The clearest match I found is Veeplay.

• Veeplay offers a client-side AI upscaling Web SDK that runs directly in the viewer's browser/GPU. You can stream a lower-resolution rendition and upscale it locally before display, reducing CDN/bandwidth consumption.
• They specifically report examples such as 360p → 720p with ~73% data savings, and 540p → 1080p with ~70% savings.
• Their approach is designed to integrate with a web video player rather than requiring the video to be pre-upscaled on the server.

There are also other approaches:

• Myelin Foundry / Fovea Stream — on-device deep-learning video enhancement specifically positioned for reducing streaming bandwidth.
• Anbox Cloud Streaming SDK — client-side upscaling using AMD FidelityFX Super Resolution; more traditional super-resolution than AI neural upscaling, but explicitly intended to transmit lower-resolution video to save bandwidth.
• WebSR — open-source WebGPU SDK for real-time browser super-resolution, which could be integrated into your own player.

If you're looking for a commercial drop-in solution for an existing web video player, Veeplay is the one I'd investigate first.